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Model uncertainty in matrix exponential spatial growth regression models

Listed author(s):
  • Manfred M. Fischer

    ()

    (Department of Socioeconomics, Vienna University of Economics and Business)

  • Philipp Piribauer

    ()

    (Department of Economics, Vienna University of Economics and Business)

This paper considers the problem of model uncertainty associated with variable selection and specification of the spatial weight matrix in spatial growth regression models in general and growth regression models based on the matrix exponential spatial specification in particular. A natural solution, supported by formal probabilistic reasoning, is the use of Bayesian model averaging which assigns probabilities on the model space and deals with model uncertainty by mixing over models, using the posterior model probabilities as weights. This paper proposes to adopt Bayesian information criterion model weights since they have computational advantages over fully Bayesian model weights. The approach is illustrated for both identifying model covariates and unveiling spatial structures present in pan-European growth data.

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File URL: https://epub.wu.ac.at/4013/1/wp158.pdf
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Paper provided by Vienna University of Economics and Business, Department of Economics in its series Department of Economics Working Papers with number wuwp158.

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Date of creation: Oct 2013
Handle: RePEc:wiw:wiwwuw:wuwp158
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Web page: http://www.wu.ac.at/economics/en

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  1. Alicja Olejnik, 2008. "Using the spatial autoregressively distributed lag model in assessing the regional convergence of per-capita income in the EU25," Papers in Regional Science, Wiley Blackwell, vol. 87(3), pages 371-384, 08.
  2. Jesús Crespo Cuaresma & Martin Feldkircher, 2013. "Spatial Filtering, Model Uncertainty And The Speed Of Income Convergence In Europe," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(4), pages 720-741, 06.
  3. Fernandez, Carmen & Ley, Eduardo & Steel, Mark F. J., 2001. "Benchmark priors for Bayesian model averaging," Journal of Econometrics, Elsevier, vol. 100(2), pages 381-427, February.
  4. Han, Xiaoyi & Lee, Lung-fei, 2013. "Bayesian estimation and model selection for spatial Durbin error model with finite distributed lags," Regional Science and Urban Economics, Elsevier, vol. 43(5), pages 816-837.
  5. Azomahou, Théophile & Mishra, Tapas, 2008. "Age dynamics and economic growth: Revisiting the nexus in a nonparametric setting," Economics Letters, Elsevier, vol. 99(1), pages 67-71, April.
  6. Winford H. Masanjala & Chris Papageorgiou, 2008. "Rough and lonely road to prosperity: a reexamination of the sources of growth in Africa using Bayesian model averaging," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 23(5), pages 671-682.
  7. Enrique Moral-Benito, 2012. "Determinants of Economic Growth: A Bayesian Panel Data Approach," The Review of Economics and Statistics, MIT Press, vol. 94(2), pages 566-579, May.
  8. Boucekkine, Raouf & de la Croix, David & Licandro, Omar, 2002. "Vintage Human Capital, Demographic Trends, and Endogenous Growth," Journal of Economic Theory, Elsevier, vol. 104(2), pages 340-375, June.
  9. Roberto Leon-Gonzalez & Daniel Montolio, 2004. "Growth, convergence and public investment. A Bayesian model averaging approach," Applied Economics, Taylor & Francis Journals, vol. 36(17), pages 1925-1936.
  10. Claeskens,Gerda & Hjort,Nils Lid, 2008. "Model Selection and Model Averaging," Cambridge Books, Cambridge University Press, number 9780521852258, October.
  11. Olivier Parent & James Lesage, 2005. "Bayesian Model Averaging for Spatial Econometric Models," Post-Print hal-00375489, HAL.
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